def evaluate_lenet5(learning_rate=0.05, n_epochs=2000, nkerns=[50], batch_size=1, window_width=4, maxSentLength=64, emb_size=300, hidden_size=200, margin=0.5, L2_weight=0.0003, update_freq=1, norm_threshold=5.0, max_truncate=40): maxSentLength=max_truncate+2*(window_width-1) model_options = locals().copy() print "model options", model_options rootPath='/mounts/data/proj/wenpeng/Dataset/WikiQACorpus/'; rng = numpy.random.RandomState(23455) datasets, vocab_size=load_wikiQA_corpus(rootPath+'vocab.txt', rootPath+'WikiQA-train.txt', rootPath+'test_filtered.txt', max_truncate,maxSentLength)#vocab_size contain train, dev and test #datasets, vocab_size=load_wikiQA_corpus(rootPath+'vocab_lower_in_word2vec.txt', rootPath+'WikiQA-train.txt', rootPath+'test_filtered.txt', maxSentLength)#vocab_size contain train, dev and test mtPath='/mounts/data/proj/wenpeng/Dataset/WikiQACorpus/MT/BLEU_NIST/' mt_train, mt_test=load_mts_wikiQA(mtPath+'result_train/concate_2mt_train.txt', mtPath+'result_test/concate_2mt_test.txt') wm_train, wm_test=load_wmf_wikiQA(rootPath+'train_word_matching_scores.txt', rootPath+'test_word_matching_scores.txt') #wm_train, wm_test=load_wmf_wikiQA(rootPath+'train_word_matching_scores_normalized.txt', rootPath+'test_word_matching_scores_normalized.txt') indices_train, trainY, trainLengths, normalized_train_length, trainLeftPad, trainRightPad= datasets[0] indices_train_l=indices_train[::2,:] indices_train_r=indices_train[1::2,:] trainLengths_l=trainLengths[::2] trainLengths_r=trainLengths[1::2] normalized_train_length_l=normalized_train_length[::2] normalized_train_length_r=normalized_train_length[1::2] trainLeftPad_l=trainLeftPad[::2] trainLeftPad_r=trainLeftPad[1::2] trainRightPad_l=trainRightPad[::2] trainRightPad_r=trainRightPad[1::2] indices_test, testY, testLengths,normalized_test_length, testLeftPad, testRightPad= datasets[1] indices_test_l=indices_test[::2,:] indices_test_r=indices_test[1::2,:] testLengths_l=testLengths[::2] testLengths_r=testLengths[1::2] normalized_test_length_l=normalized_test_length[::2] normalized_test_length_r=normalized_test_length[1::2] testLeftPad_l=testLeftPad[::2] testLeftPad_r=testLeftPad[1::2] testRightPad_l=testRightPad[::2] testRightPad_r=testRightPad[1::2] n_train_batches=indices_train_l.shape[0]/batch_size n_test_batches=indices_test_l.shape[0]/batch_size train_batch_start=list(numpy.arange(n_train_batches)*batch_size) test_batch_start=list(numpy.arange(n_test_batches)*batch_size) indices_train_l=theano.shared(numpy.asarray(indices_train_l, dtype=theano.config.floatX), borrow=True) indices_train_r=theano.shared(numpy.asarray(indices_train_r, dtype=theano.config.floatX), borrow=True) indices_test_l=theano.shared(numpy.asarray(indices_test_l, dtype=theano.config.floatX), borrow=True) indices_test_r=theano.shared(numpy.asarray(indices_test_r, dtype=theano.config.floatX), borrow=True) indices_train_l=T.cast(indices_train_l, 'int64') indices_train_r=T.cast(indices_train_r, 'int64') indices_test_l=T.cast(indices_test_l, 'int64') indices_test_r=T.cast(indices_test_r, 'int64') rand_values=random_value_normal((vocab_size+1, emb_size), theano.config.floatX, numpy.random.RandomState(1234)) rand_values[0]=numpy.array(numpy.zeros(emb_size),dtype=theano.config.floatX) #rand_values[0]=numpy.array([1e-50]*emb_size) rand_values=load_word2vec_to_init(rand_values, rootPath+'vocab_embs_300d.txt') #rand_values=load_word2vec_to_init(rand_values, rootPath+'vocab_lower_in_word2vec_embs_300d.txt') embeddings=theano.shared(value=rand_values, borrow=True) #cost_tmp=0 error_sum=0 # allocate symbolic variables for the data index = T.lscalar() x_index_l = T.lmatrix('x_index_l') # now, x is the index matrix, must be integer x_index_r = T.lmatrix('x_index_r') y = T.lvector('y') left_l=T.lscalar() right_l=T.lscalar() left_r=T.lscalar() right_r=T.lscalar() length_l=T.lscalar() length_r=T.lscalar() norm_length_l=T.dscalar() norm_length_r=T.dscalar() mts=T.dmatrix() wmf=T.dmatrix() cost_tmp=T.dscalar() #x=embeddings[x_index.flatten()].reshape(((batch_size*4),maxSentLength, emb_size)).transpose(0, 2, 1).flatten() ishape = (emb_size, maxSentLength) # this is the size of MNIST images filter_size=(emb_size,window_width) #poolsize1=(1, ishape[1]-filter_size[1]+1) #????????????????????????????? length_after_wideConv=ishape[1]+filter_size[1]-1 ###################### # BUILD ACTUAL MODEL # ###################### print '... building the model' # Reshape matrix of rasterized images of shape (batch_size,28*28) # to a 4D tensor, compatible with our LeNetConvPoolLayer #layer0_input = x.reshape(((batch_size*4), 1, ishape[0], ishape[1])) layer0_l_input = embeddings[x_index_l.flatten()].reshape((batch_size,maxSentLength, emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2) layer0_r_input = embeddings[x_index_r.flatten()].reshape((batch_size,maxSentLength, emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2) conv_W, conv_b=create_conv_para(rng, filter_shape=(nkerns[0], 1, filter_size[0], filter_size[1])) #layer0_output = debug_print(layer0.output, 'layer0.output') layer0_l = Conv_with_input_para(rng, input=layer0_l_input, image_shape=(batch_size, 1, ishape[0], ishape[1]), filter_shape=(nkerns[0], 1, filter_size[0], filter_size[1]), W=conv_W, b=conv_b) layer0_r = Conv_with_input_para(rng, input=layer0_r_input, image_shape=(batch_size, 1, ishape[0], ishape[1]), filter_shape=(nkerns[0], 1, filter_size[0], filter_size[1]), W=conv_W, b=conv_b) layer0_l_output=debug_print(layer0_l.output, 'layer0_l.output') layer0_r_output=debug_print(layer0_r.output, 'layer0_r.output') layer1=Average_Pooling_for_Top(rng, input_l=layer0_l_output, input_r=layer0_r_output, kern=nkerns[0], left_l=left_l, right_l=right_l, left_r=left_r, right_r=right_r, length_l=length_l+filter_size[1]-1, length_r=length_r+filter_size[1]-1, dim=maxSentLength+filter_size[1]-1) #layer2=HiddenLayer(rng, input=layer1_out, n_in=nkerns[0]*2, n_out=hidden_size, activation=T.tanh) sum_uni_l=T.sum(layer0_l_input, axis=3).reshape((1, emb_size)) aver_uni_l=sum_uni_l/layer0_l_input.shape[3] norm_uni_l=sum_uni_l/T.sqrt((sum_uni_l**2).sum()) sum_uni_r=T.sum(layer0_r_input, axis=3).reshape((1, emb_size)) aver_uni_r=sum_uni_r/layer0_r_input.shape[3] norm_uni_r=sum_uni_r/T.sqrt((sum_uni_r**2).sum()) uni_cosine=cosine(sum_uni_l, sum_uni_r) aver_uni_cosine=cosine(aver_uni_l, aver_uni_r) uni_sigmoid_simi=debug_print(T.nnet.sigmoid(T.dot(norm_uni_l, norm_uni_r.T)).reshape((1,1)),'uni_sigmoid_simi') ''' linear=Linear(sum_uni_l, sum_uni_r) poly=Poly(sum_uni_l, sum_uni_r) sigmoid=Sigmoid(sum_uni_l, sum_uni_r) rbf=RBF(sum_uni_l, sum_uni_r) gesd=GESD(sum_uni_l, sum_uni_r) ''' eucli_1=1.0/(1.0+EUCLID(sum_uni_l, sum_uni_r))#25.2% #eucli_1_exp=1.0/T.exp(EUCLID(sum_uni_l, sum_uni_r)) len_l=norm_length_l.reshape((1,1)) len_r=norm_length_r.reshape((1,1)) ''' len_l=length_l.reshape((1,1)) len_r=length_r.reshape((1,1)) ''' #length_gap=T.log(1+(T.sqrt((len_l-len_r)**2))).reshape((1,1)) #length_gap=T.sqrt((len_l-len_r)**2) #layer3_input=mts layer3_input=T.concatenate([#mts, uni_cosine,#eucli_1_exp,#uni_sigmoid_simi, #norm_uni_l-(norm_uni_l+norm_uni_r)/2,#uni_cosine, # layer1.output_cosine, #layer1.output_eucli_to_simi_exp,#layer1.output_sigmoid_simi,#layer1.output_vector_l-(layer1.output_vector_l+layer1.output_vector_r)/2,#layer1.output_cosine, # len_l, len_r,wmf ], axis=1)#, layer2.output, layer1.output_cosine], axis=1) #layer3_input=T.concatenate([mts,eucli, uni_cosine, len_l, len_r, norm_uni_l-(norm_uni_l+norm_uni_r)/2], axis=1) #layer3=LogisticRegression(rng, input=layer3_input, n_in=11, n_out=2) layer3=LogisticRegression(rng, input=layer3_input, n_in=(1)+(1)+2+2, n_out=2) #L2_reg =(layer3.W** 2).sum()+(layer2.W** 2).sum()+(layer1.W** 2).sum()+(conv_W** 2).sum() L2_reg =debug_print((layer3.W** 2).sum()+(conv_W** 2).sum(), 'L2_reg')#+(layer1.W** 2).sum()++(embeddings**2).sum() cost_this =debug_print(layer3.negative_log_likelihood(y), 'cost_this')#+L2_weight*L2_reg cost=debug_print((cost_this+cost_tmp)/update_freq+L2_weight*L2_reg, 'cost') #cost=debug_print((cost_this+cost_tmp)/update_freq, 'cost') test_model = theano.function([index], [layer3.prop_for_posi,layer3_input, y], givens={ x_index_l: indices_test_l[index: index + batch_size], x_index_r: indices_test_r[index: index + batch_size], y: testY[index: index + batch_size], left_l: testLeftPad_l[index], right_l: testRightPad_l[index], left_r: testLeftPad_r[index], right_r: testRightPad_r[index], length_l: testLengths_l[index], length_r: testLengths_r[index], norm_length_l: normalized_test_length_l[index], norm_length_r: normalized_test_length_r[index], mts: mt_test[index: index + batch_size], wmf: wm_test[index: index + batch_size]}, on_unused_input='ignore') #params = layer3.params + layer2.params + layer1.params+ [conv_W, conv_b] params = layer3.params+ [conv_W, conv_b]#+[embeddings]# + layer1.params params_conv = [conv_W, conv_b] accumulator=[] for para_i in params: eps_p=numpy.zeros_like(para_i.get_value(borrow=True),dtype=theano.config.floatX) accumulator.append(theano.shared(eps_p, borrow=True)) # create a list of gradients for all model parameters grads = T.grad(cost, params) updates = [] for param_i, grad_i, acc_i in zip(params, grads, accumulator): grad_i=debug_print(grad_i,'grad_i') acc = acc_i + T.sqr(grad_i) updates.append((param_i, param_i - learning_rate * grad_i / T.sqrt(acc))) #AdaGrad updates.append((acc_i, acc)) train_model = theano.function([index,cost_tmp], cost, updates=updates, givens={ x_index_l: indices_train_l[index: index + batch_size], x_index_r: indices_train_r[index: index + batch_size], y: trainY[index: index + batch_size], left_l: trainLeftPad_l[index], right_l: trainRightPad_l[index], left_r: trainLeftPad_r[index], right_r: trainRightPad_r[index], length_l: trainLengths_l[index], length_r: trainLengths_r[index], norm_length_l: normalized_train_length_l[index], norm_length_r: normalized_train_length_r[index], mts: mt_train[index: index + batch_size], wmf: wm_train[index: index + batch_size]}, on_unused_input='ignore') train_model_predict = theano.function([index], [cost_this,layer3.errors(y), layer3_input, y], givens={ x_index_l: indices_train_l[index: index + batch_size], x_index_r: indices_train_r[index: index + batch_size], y: trainY[index: index + batch_size], left_l: trainLeftPad_l[index], right_l: trainRightPad_l[index], left_r: trainLeftPad_r[index], right_r: trainRightPad_r[index], length_l: trainLengths_l[index], length_r: trainLengths_r[index], norm_length_l: normalized_train_length_l[index], norm_length_r: normalized_train_length_r[index], mts: mt_train[index: index + batch_size], wmf: wm_train[index: index + batch_size]}, on_unused_input='ignore') ############### # TRAIN MODEL # ############### print '... training' # early-stopping parameters patience = 500000000000000 # look as this many examples regardless patience_increase = 2 # wait this much longer when a new best is # found improvement_threshold = 0.995 # a relative improvement of this much is # considered significant validation_frequency = min(n_train_batches, patience / 2) # go through this many # minibatche before checking the network # on the validation set; in this case we # check every epoch best_params = None best_validation_loss = numpy.inf best_iter = 0 test_score = 0. start_time = time.clock() epoch = 0 done_looping = False svm_max=0.0 best_epoch=0 while (epoch < n_epochs) and (not done_looping): epoch = epoch + 1 #for minibatch_index in xrange(n_train_batches): # each batch minibatch_index=0 #shuffle(train_batch_start)#shuffle training data cost_tmp=0.0 for batch_start in train_batch_start: # iter means how many batches have been runed, taking into loop iter = (epoch - 1) * n_train_batches + minibatch_index +1 minibatch_index=minibatch_index+1 #if epoch %2 ==0: # batch_start=batch_start+remain_train #time.sleep(0.5) #print batch_start if iter%update_freq != 0: cost_ij, error_ij, layer3_input, y=train_model_predict(batch_start) #print 'layer3_input', layer3_input cost_tmp+=cost_ij error_sum+=error_ij #print 'cost_acc ',cost_acc #print 'cost_ij ', cost_ij #print 'cost_tmp before update',cost_tmp else: cost_average= train_model(batch_start,cost_tmp) #print 'layer3_input', layer3_input error_sum=0 cost_tmp=0.0#reset for the next batch #print 'cost_average ', cost_average #print 'cost_this ',cost_this #exit(0) #exit(0) if iter % n_train_batches == 0: print 'training @ iter = '+str(iter)+' average cost: '+str(cost_average)+' error: '+str(error_sum)+'/'+str(update_freq)+' error rate: '+str(error_sum*1.0/update_freq) #if iter ==1: # exit(0) if iter % validation_frequency == 0: #write_file=open('log.txt', 'w') test_probs=[] test_y=[] test_features=[] for i in test_batch_start: prob_i, layer3_input, y=test_model(i) #test_losses = [test_model(i) for i in test_batch_start] test_probs.append(prob_i[0][0]) test_y.append(y[0]) test_features.append(layer3_input[0]) MAP, MRR=compute_map_mrr(rootPath+'test_filtered.txt', test_probs) #now, check MAP and MRR print(('\t\t\t\t\t\tepoch %i, minibatch %i/%i, test MAP of best ' 'model %f, MRR %f') % (epoch, minibatch_index, n_train_batches,MAP, MRR)) #now, see the results of LR #write_feature=open(rootPath+'feature_check.txt', 'w') train_y=[] train_features=[] count=0 for batch_start in train_batch_start: cost_ij, error_ij, layer3_input, y=train_model_predict(batch_start) train_y.append(y[0]) train_features.append(layer3_input[0]) #write_feature.write(str(batch_start)+' '+' '.join(map(str,layer3_input[0]))+'\n') #count+=1 #write_feature.close() clf = svm.SVC(C=1.0, kernel='linear') clf.fit(train_features, train_y) results_svm=clf.decision_function(test_features) MAP_svm, MRR_svm=compute_map_mrr(rootPath+'test_filtered.txt', results_svm) lr=LinearRegression().fit(train_features, train_y) results_lr=lr.predict(test_features) MAP_lr, MRR_lr=compute_map_mrr(rootPath+'test_filtered.txt', results_lr) print '\t\t\t\t\t\t\tSVM, MAP: ', MAP_svm, ' MRR: ', MRR_svm, ' LR: ', MAP_lr, ' MRR: ', MRR_lr if patience <= iter: done_looping = True break #after each epoch, increase the batch_size if epoch%2==1: update_freq=update_freq*1 else: update_freq=update_freq/1 #store the paras after epoch 15 if epoch ==15: store_model_to_file(params_conv) print 'Finished storing best conv params' exit(0) #print 'Batch_size: ', update_freq end_time = time.clock() print('Optimization complete.') print('Best validation score of %f %% obtained at iteration %i,'\ 'with test performance %f %%' % (best_validation_loss * 100., best_iter + 1, test_score * 100.)) print >> sys.stderr, ('The code for file ' + os.path.split(__file__)[1] + ' ran for %.2fm' % ((end_time - start_time) / 60.))
def evaluate_lenet5(learning_rate=0.085, n_epochs=2000, nkerns=[50], batch_size=1, window_width=3, maxSentLength=60, emb_size=300, hidden_size=200, margin=0.5, L2_weight=0.0001, update_freq=1, norm_threshold=5.0): model_options = locals().copy() print "model options", model_options rootPath='/mounts/data/proj/wenpeng/Dataset/MicrosoftParaphrase/tokenized_msr/'; rng = numpy.random.RandomState(23455) datasets, vocab_size=load_msr_corpus(rootPath+'vocab.txt', rootPath+'tokenized_train.txt', rootPath+'tokenized_test.txt', maxSentLength) mtPath='/mounts/data/proj/wenpeng/Dataset/paraphraseMT/' mt_train, mt_test=load_mts(mtPath+'concate_15mt_train.txt', mtPath+'concate_15mt_test.txt') wm_train, wm_test=load_wmf_wikiQA(rootPath+'train_word_matching_scores_normalized.txt', rootPath+'test_word_matching_scores_normalized.txt') indices_train, trainY, trainLengths, normalized_train_length, trainLeftPad, trainRightPad= datasets[0] indices_train_l=indices_train[::2,:] indices_train_r=indices_train[1::2,:] trainLengths_l=trainLengths[::2] trainLengths_r=trainLengths[1::2] normalized_train_length_l=normalized_train_length[::2] normalized_train_length_r=normalized_train_length[1::2] trainLeftPad_l=trainLeftPad[::2] trainLeftPad_r=trainLeftPad[1::2] trainRightPad_l=trainRightPad[::2] trainRightPad_r=trainRightPad[1::2] indices_test, testY, testLengths,normalized_test_length, testLeftPad, testRightPad= datasets[1] indices_test_l=indices_test[::2,:] indices_test_r=indices_test[1::2,:] testLengths_l=testLengths[::2] testLengths_r=testLengths[1::2] normalized_test_length_l=normalized_test_length[::2] normalized_test_length_r=normalized_test_length[1::2] testLeftPad_l=testLeftPad[::2] testLeftPad_r=testLeftPad[1::2] testRightPad_l=testRightPad[::2] testRightPad_r=testRightPad[1::2] n_train_batches=indices_train_l.shape[0]/batch_size n_test_batches=indices_test_l.shape[0]/batch_size train_batch_start=list(numpy.arange(n_train_batches)*batch_size) test_batch_start=list(numpy.arange(n_test_batches)*batch_size) indices_train_l=theano.shared(numpy.asarray(indices_train_l, dtype=theano.config.floatX), borrow=True) indices_train_r=theano.shared(numpy.asarray(indices_train_r, dtype=theano.config.floatX), borrow=True) indices_test_l=theano.shared(numpy.asarray(indices_test_l, dtype=theano.config.floatX), borrow=True) indices_test_r=theano.shared(numpy.asarray(indices_test_r, dtype=theano.config.floatX), borrow=True) indices_train_l=T.cast(indices_train_l, 'int64') indices_train_r=T.cast(indices_train_r, 'int64') indices_test_l=T.cast(indices_test_l, 'int64') indices_test_r=T.cast(indices_test_r, 'int64') rand_values=random_value_normal((vocab_size+1, emb_size), theano.config.floatX, numpy.random.RandomState(1234)) rand_values[0]=numpy.array(numpy.zeros(emb_size)) #rand_values[0]=numpy.array([1e-50]*emb_size) rand_values=load_word2vec_to_init(rand_values, rootPath+'vocab_embs_300d.txt') embeddings=theano.shared(value=rand_values, borrow=True) cost_tmp=0 error_sum=0 # allocate symbolic variables for the data index = T.lscalar() x_index_l = T.lmatrix('x_index_l') # now, x is the index matrix, must be integer x_index_r = T.lmatrix('x_index_r') y = T.lvector('y') left_l=T.lscalar() right_l=T.lscalar() left_r=T.lscalar() right_r=T.lscalar() length_l=T.lscalar() length_r=T.lscalar() norm_length_l=T.dscalar() norm_length_r=T.dscalar() mts=T.dmatrix() wmf=T.dmatrix() cost_tmp=T.dscalar() #x=embeddings[x_index.flatten()].reshape(((batch_size*4),maxSentLength, emb_size)).transpose(0, 2, 1).flatten() ishape = (emb_size, maxSentLength) # this is the size of MNIST images filter_size=(emb_size,window_width) #poolsize1=(1, ishape[1]-filter_size[1]+1) #????????????????????????????? length_after_wideConv=ishape[1]+filter_size[1]-1 ###################### # BUILD ACTUAL MODEL # ###################### print '... building the model' # Reshape matrix of rasterized images of shape (batch_size,28*28) # to a 4D tensor, compatible with our LeNetConvPoolLayer #layer0_input = x.reshape(((batch_size*4), 1, ishape[0], ishape[1])) layer0_l_input = embeddings[x_index_l.flatten()].reshape((batch_size,maxSentLength, emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2) layer0_r_input = embeddings[x_index_r.flatten()].reshape((batch_size,maxSentLength, emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2) conv_W, conv_b=create_conv_para(rng, filter_shape=(nkerns[0], 1, filter_size[0], filter_size[1])) #layer0_output = debug_print(layer0.output, 'layer0.output') layer0_l = Conv_with_input_para(rng, input=layer0_l_input, image_shape=(batch_size, 1, ishape[0], ishape[1]), filter_shape=(nkerns[0], 1, filter_size[0], filter_size[1]), W=conv_W, b=conv_b) layer0_r = Conv_with_input_para(rng, input=layer0_r_input, image_shape=(batch_size, 1, ishape[0], ishape[1]), filter_shape=(nkerns[0], 1, filter_size[0], filter_size[1]), W=conv_W, b=conv_b) layer0_l_output=debug_print(layer0_l.output, 'layer0_l.output') layer0_r_output=debug_print(layer0_r.output, 'layer0_r.output') layer1=Average_Pooling_for_Top(rng, input_l=layer0_l_output, input_r=layer0_r_output, kern=nkerns[0], left_l=left_l, right_l=right_l, left_r=left_r, right_r=right_r, length_l=length_l+filter_size[1]-1, length_r=length_r+filter_size[1]-1, dim=maxSentLength+filter_size[1]-1) #layer2=HiddenLayer(rng, input=layer1_out, n_in=nkerns[0]*2, n_out=hidden_size, activation=T.tanh) sum_uni_l=T.sum(layer0_l_input, axis=3).reshape((1, emb_size)) norm_uni_l=sum_uni_l/T.sqrt((sum_uni_l**2).sum()) sum_uni_r=T.sum(layer0_r_input, axis=3).reshape((1, emb_size)) norm_uni_r=sum_uni_r/T.sqrt((sum_uni_r**2).sum()) uni_cosine=cosine(sum_uni_l, sum_uni_r) ''' linear=Linear(sum_uni_l, sum_uni_r) poly=Poly(sum_uni_l, sum_uni_r) sigmoid=Sigmoid(sum_uni_l, sum_uni_r) rbf=RBF(sum_uni_l, sum_uni_r) gesd=GESD(sum_uni_l, sum_uni_r) ''' eucli_1=1.0/(1.0+EUCLID(sum_uni_l, sum_uni_r))#25.2% #eucli_1=EUCLID(sum_uni_l, sum_uni_r) len_l=norm_length_l.reshape((1,1)) len_r=norm_length_r.reshape((1,1)) ''' len_l=length_l.reshape((1,1)) len_r=length_r.reshape((1,1)) ''' #length_gap=T.log(1+(T.sqrt((len_l-len_r)**2))).reshape((1,1)) #length_gap=T.sqrt((len_l-len_r)**2) #layer3_input=mts layer3_input=T.concatenate([mts, eucli_1, #uni_cosine,#norm_uni_l-(norm_uni_l+norm_uni_r)/2,#uni_cosine, # layer1.output_eucli_to_simi, #layer1.output_cosine,#layer1.output_vector_l-(layer1.output_vector_l+layer1.output_vector_r)/2,#layer1.output_cosine, # len_l, len_r, #layer1.output_attentions, #wmf, ], axis=1)#, layer2.output, layer1.output_cosine], axis=1) #layer3_input=T.concatenate([mts,eucli, uni_cosine, len_l, len_r, norm_uni_l-(norm_uni_l+norm_uni_r)/2], axis=1) #layer3=LogisticRegression(rng, input=layer3_input, n_in=11, n_out=2) layer3=LogisticRegression(rng, input=layer3_input, n_in=15+(2)+(2)+2, n_out=2) #L2_reg =(layer3.W** 2).sum()+(layer2.W** 2).sum()+(layer1.W** 2).sum()+(conv_W** 2).sum() L2_reg =debug_print((layer3.W** 2).sum()+(conv_W** 2).sum(), 'L2_reg')#+(layer1.W** 2).sum() cost_this =debug_print(layer3.negative_log_likelihood(y), 'cost_this')#+L2_weight*L2_reg cost=debug_print((cost_this+cost_tmp)/update_freq+L2_weight*L2_reg, 'cost') test_model = theano.function([index], [layer3.errors(y), layer3.y_pred, layer3_input, y], givens={ x_index_l: indices_test_l[index: index + batch_size], x_index_r: indices_test_r[index: index + batch_size], y: testY[index: index + batch_size], left_l: testLeftPad_l[index], right_l: testRightPad_l[index], left_r: testLeftPad_r[index], right_r: testRightPad_r[index], length_l: testLengths_l[index], length_r: testLengths_r[index], norm_length_l: normalized_test_length_l[index], norm_length_r: normalized_test_length_r[index], mts: mt_test[index: index + batch_size], wmf: wm_test[index: index + batch_size]}, on_unused_input='ignore') #params = layer3.params + layer2.params + layer1.params+ [conv_W, conv_b] params = layer3.params+ [conv_W, conv_b]# + layer1.params accumulator=[] for para_i in params: eps_p=numpy.zeros_like(para_i.get_value(borrow=True),dtype=theano.config.floatX) accumulator.append(theano.shared(eps_p, borrow=True)) # create a list of gradients for all model parameters grads = T.grad(cost, params) updates = [] for param_i, grad_i, acc_i in zip(params, grads, accumulator): #grad_i=debug_print(grad_i,'grad_i') #norm=T.sqrt((grad_i**2).sum()) #if T.lt(norm_threshold, norm): # print 'big norm' # grad_i=grad_i*(norm_threshold/norm) acc = acc_i + T.sqr(grad_i) updates.append((param_i, param_i - learning_rate * grad_i / T.sqrt(acc))) #AdaGrad updates.append((acc_i, acc)) train_model = theano.function([index,cost_tmp], [cost,layer3.errors(y), layer3_input], updates=updates, givens={ x_index_l: indices_train_l[index: index + batch_size], x_index_r: indices_train_r[index: index + batch_size], y: trainY[index: index + batch_size], left_l: trainLeftPad_l[index], right_l: trainRightPad_l[index], left_r: trainLeftPad_r[index], right_r: trainRightPad_r[index], length_l: trainLengths_l[index], length_r: trainLengths_r[index], norm_length_l: normalized_train_length_l[index], norm_length_r: normalized_train_length_r[index], mts: mt_train[index: index + batch_size], wmf: wm_train[index: index + batch_size]}, on_unused_input='ignore') train_model_predict = theano.function([index], [cost_this,layer3.errors(y), layer3_input, y , sum_uni_l, sum_uni_r, uni_cosine], givens={ x_index_l: indices_train_l[index: index + batch_size], x_index_r: indices_train_r[index: index + batch_size], y: trainY[index: index + batch_size], left_l: trainLeftPad_l[index], right_l: trainRightPad_l[index], left_r: trainLeftPad_r[index], right_r: trainRightPad_r[index], length_l: trainLengths_l[index], length_r: trainLengths_r[index], norm_length_l: normalized_train_length_l[index], norm_length_r: normalized_train_length_r[index], mts: mt_train[index: index + batch_size], wmf: wm_train[index: index + batch_size]}, on_unused_input='ignore') ############### # TRAIN MODEL # ############### print '... training' # early-stopping parameters patience = 500000000000000 # look as this many examples regardless patience_increase = 2 # wait this much longer when a new best is # found improvement_threshold = 0.995 # a relative improvement of this much is # considered significant validation_frequency = min(n_train_batches/5, patience / 2) # go through this many # minibatche before checking the network # on the validation set; in this case we # check every epoch best_params = None best_validation_loss = numpy.inf best_iter = 0 test_score = 0. start_time = time.clock() epoch = 0 done_looping = False max_acc=0.0 best_epoch=0 while (epoch < n_epochs) and (not done_looping): epoch = epoch + 1 #for minibatch_index in xrange(n_train_batches): # each batch minibatch_index=0 #shuffle(train_batch_start)#shuffle training data cost_tmp=0.0 for batch_start in train_batch_start: # iter means how many batches have been runed, taking into loop iter = (epoch - 1) * n_train_batches + minibatch_index +1 minibatch_index=minibatch_index+1 #if epoch %2 ==0: # batch_start=batch_start+remain_train #time.sleep(0.5) if iter%update_freq != 0: cost_ij, error_ij, layer3_input, y, sum_uni_l, sum_uni_r, uni_cosine=train_model_predict(batch_start) #print 'cost_ij: ', cost_ij cost_tmp+=cost_ij error_sum+=error_ij else: cost_average, error_ij, layer3_input= train_model(batch_start,cost_tmp) #print 'training @ iter = '+str(iter)+' average cost: '+str(cost_average)+' sum error: '+str(error_sum)+'/'+str(update_freq) error_sum=0 cost_tmp=0.0#reset for the next batch #print layer3_input #exit(0) #exit(0) if iter % n_train_batches == 0: print 'training @ iter = '+str(iter)+' average cost: '+str(cost_average)+' error: '+str(error_sum)+'/'+str(update_freq)+' error rate: '+str(error_sum*1.0/update_freq) #if iter ==1: # exit(0) if iter % validation_frequency == 0: #write_file=open('log.txt', 'w') test_losses=[] for i in test_batch_start: test_loss, pred_y, layer3_input, y=test_model(i) #test_losses = [test_model(i) for i in test_batch_start] test_losses.append(test_loss) #write_file.write(str(pred_y[0])+'\n')#+'\t'+str(testY[i].eval())+ #write_file.close() test_score = numpy.mean(test_losses) print(('\t\t\t\t\t\tepoch %i, minibatch %i/%i, test acc of best ' 'model %f %%') % (epoch, minibatch_index, n_train_batches, (1-test_score) * 100.)) #now, see the results of svm write_feature=open('feature_check.txt', 'w') train_y=[] train_features=[] for batch_start in train_batch_start: cost_ij, error_ij, layer3_input, y, sum_uni_l, sum_uni_r, uni_cosine=train_model_predict(batch_start) train_y.append(y[0]) train_features.append(layer3_input[0]) write_feature.write(' '.join(map(str,layer3_input[0]))+'\n') write_feature.close() test_y=[] test_features=[] for i in test_batch_start: test_loss, pred_y, layer3_input, y=test_model(i) test_y.append(y[0]) test_features.append(layer3_input[0]) clf = svm.SVC(kernel='linear')#OneVsRestClassifier(LinearSVC()) #linear 76.11%, poly 75.19, sigmoid 66.50, rbf 73.33 clf.fit(train_features, train_y) results=clf.predict(test_features) lr=LinearRegression().fit(train_features, train_y) results_lr=lr.predict(test_features) corr_count=0 corr_lr=0 test_size=len(test_y) for i in range(test_size): if results[i]==test_y[i]: corr_count+=1 if numpy.absolute(results_lr[i]-test_y[i])<0.5: corr_lr+=1 acc=corr_count*1.0/test_size acc_lr=corr_lr*1.0/test_size if acc > max_acc: max_acc=acc best_epoch=epoch if acc_lr> max_acc: max_acc=acc_lr best_epoch=epoch print '\t\t\t\t\t\t\t\t\t\t\tsvm acc: ', acc, 'LR acc: ', acc_lr, ' max acc: ', max_acc , ' at epoch: ', best_epoch #exit(0) if patience <= iter: done_looping = True break #after each epoch, increase the batch_size if epoch%2==1: update_freq=update_freq*1 else: update_freq=update_freq/1 #print 'Batch_size: ', update_freq end_time = time.clock() print('Optimization complete.') print('Best validation score of %f %% obtained at iteration %i,'\ 'with test performance %f %%' % (best_validation_loss * 100., best_iter + 1, test_score * 100.)) print >> sys.stderr, ('The code for file ' + os.path.split(__file__)[1] + ' ran for %.2fm' % ((end_time - start_time) / 60.))
def evaluate_lenet5(learning_rate=0.06, n_epochs=2000, nkerns=[50, 50], batch_size=1, window_width=[4, 4], maxSentLength=64, emb_size=300, hidden_size=200, margin=0.5, L2_weight=0.0006, update_freq=1, norm_threshold=5.0, max_truncate=40): maxSentLength = max_truncate + 2 * (window_width[0] - 1) model_options = locals().copy() print "model options", model_options rootPath = '/mounts/data/proj/wenpeng/Dataset/WikiQACorpus/' rng = numpy.random.RandomState(23455) datasets, vocab_size = load_wikiQA_corpus( rootPath + 'vocab.txt', rootPath + 'WikiQA-train.txt', rootPath + 'test_filtered.txt', max_truncate, maxSentLength) #vocab_size contain train, dev and test #datasets, vocab_size=load_wikiQA_corpus(rootPath+'vocab_lower_in_word2vec.txt', rootPath+'WikiQA-train.txt', rootPath+'test_filtered.txt', maxSentLength)#vocab_size contain train, dev and test mtPath = '/mounts/data/proj/wenpeng/Dataset/WikiQACorpus/MT/BLEU_NIST/' mt_train, mt_test = load_mts_wikiQA( mtPath + 'result_train/concate_2mt_train.txt', mtPath + 'result_test/concate_2mt_test.txt') wm_train, wm_test = load_wmf_wikiQA( rootPath + 'train_word_matching_scores.txt', rootPath + 'test_word_matching_scores.txt') #wm_train, wm_test=load_wmf_wikiQA(rootPath+'train_word_matching_scores_normalized.txt', rootPath+'test_word_matching_scores_normalized.txt') indices_train, trainY, trainLengths, normalized_train_length, trainLeftPad, trainRightPad = datasets[ 0] indices_train_l = indices_train[::2, :] indices_train_r = indices_train[1::2, :] trainLengths_l = trainLengths[::2] trainLengths_r = trainLengths[1::2] normalized_train_length_l = normalized_train_length[::2] normalized_train_length_r = normalized_train_length[1::2] trainLeftPad_l = trainLeftPad[::2] trainLeftPad_r = trainLeftPad[1::2] trainRightPad_l = trainRightPad[::2] trainRightPad_r = trainRightPad[1::2] indices_test, testY, testLengths, normalized_test_length, testLeftPad, testRightPad = datasets[ 1] indices_test_l = indices_test[::2, :] indices_test_r = indices_test[1::2, :] testLengths_l = testLengths[::2] testLengths_r = testLengths[1::2] normalized_test_length_l = normalized_test_length[::2] normalized_test_length_r = normalized_test_length[1::2] testLeftPad_l = testLeftPad[::2] testLeftPad_r = testLeftPad[1::2] testRightPad_l = testRightPad[::2] testRightPad_r = testRightPad[1::2] n_train_batches = indices_train_l.shape[0] / batch_size n_test_batches = indices_test_l.shape[0] / batch_size train_batch_start = list(numpy.arange(n_train_batches) * batch_size) test_batch_start = list(numpy.arange(n_test_batches) * batch_size) indices_train_l = theano.shared(numpy.asarray(indices_train_l, dtype=theano.config.floatX), borrow=True) indices_train_r = theano.shared(numpy.asarray(indices_train_r, dtype=theano.config.floatX), borrow=True) indices_test_l = theano.shared(numpy.asarray(indices_test_l, dtype=theano.config.floatX), borrow=True) indices_test_r = theano.shared(numpy.asarray(indices_test_r, dtype=theano.config.floatX), borrow=True) indices_train_l = T.cast(indices_train_l, 'int64') indices_train_r = T.cast(indices_train_r, 'int64') indices_test_l = T.cast(indices_test_l, 'int64') indices_test_r = T.cast(indices_test_r, 'int64') rand_values = random_value_normal((vocab_size + 1, emb_size), theano.config.floatX, numpy.random.RandomState(1234)) rand_values[0] = numpy.array(numpy.zeros(emb_size), dtype=theano.config.floatX) #rand_values[0]=numpy.array([1e-50]*emb_size) rand_values = load_word2vec_to_init(rand_values, rootPath + 'vocab_embs_300d.txt') #rand_values=load_word2vec_to_init(rand_values, rootPath+'vocab_lower_in_word2vec_embs_300d.txt') embeddings = theano.shared(value=rand_values, borrow=True) #cost_tmp=0 error_sum = 0 # allocate symbolic variables for the data index = T.lscalar() x_index_l = T.lmatrix( 'x_index_l') # now, x is the index matrix, must be integer x_index_r = T.lmatrix('x_index_r') y = T.lvector('y') left_l = T.lscalar() right_l = T.lscalar() left_r = T.lscalar() right_r = T.lscalar() length_l = T.lscalar() length_r = T.lscalar() norm_length_l = T.dscalar() norm_length_r = T.dscalar() mts = T.dmatrix() wmf = T.dmatrix() cost_tmp = T.dscalar() #x=embeddings[x_index.flatten()].reshape(((batch_size*4),maxSentLength, emb_size)).transpose(0, 2, 1).flatten() ishape = (emb_size, maxSentLength) # this is the size of MNIST images filter_size = (emb_size, window_width[0]) filter_size_2 = (nkerns[0], window_width[1]) #poolsize1=(1, ishape[1]-filter_size[1]+1) #????????????????????????????? length_after_wideConv = ishape[1] + filter_size[1] - 1 ###################### # BUILD ACTUAL MODEL # ###################### print '... building the model' # Reshape matrix of rasterized images of shape (batch_size,28*28) # to a 4D tensor, compatible with our LeNetConvPoolLayer #layer0_input = x.reshape(((batch_size*4), 1, ishape[0], ishape[1])) layer0_l_input = embeddings[x_index_l.flatten()].reshape( (batch_size, maxSentLength, emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2) layer0_r_input = embeddings[x_index_r.flatten()].reshape( (batch_size, maxSentLength, emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2) conv_W, conv_b = create_conv_para(rng, filter_shape=(nkerns[0], 1, filter_size[0], filter_size[1])) load_model_from_file([conv_W, conv_b]) #layer0_output = debug_print(layer0.output, 'layer0.output') layer0_l = Conv_with_input_para(rng, input=layer0_l_input, image_shape=(batch_size, 1, ishape[0], ishape[1]), filter_shape=(nkerns[0], 1, filter_size[0], filter_size[1]), W=conv_W, b=conv_b) layer0_r = Conv_with_input_para(rng, input=layer0_r_input, image_shape=(batch_size, 1, ishape[0], ishape[1]), filter_shape=(nkerns[0], 1, filter_size[0], filter_size[1]), W=conv_W, b=conv_b) layer0_l_output = debug_print(layer0_l.output, 'layer0_l.output') layer0_r_output = debug_print(layer0_r.output, 'layer0_r.output') layer0_para = [conv_W, conv_b] layer1 = Average_Pooling(rng, input_l=layer0_l_output, input_r=layer0_r_output, kern=nkerns[0], left_l=left_l, right_l=right_l, left_r=left_r, right_r=right_r, length_l=length_l + filter_size[1] - 1, length_r=length_r + filter_size[1] - 1, dim=maxSentLength + filter_size[1] - 1, window_size=window_width[0], maxSentLength=maxSentLength) conv2_W, conv2_b = create_conv_para(rng, filter_shape=(nkerns[1], 1, filter_size_2[0], filter_size_2[1])) #load_model_from_file([conv2_W, conv2_b]) layer2_l = Conv_with_input_para( rng, input=layer1.output_tensor_l, image_shape=(batch_size, 1, nkerns[0], ishape[1]), filter_shape=(nkerns[1], 1, filter_size_2[0], filter_size_2[1]), W=conv2_W, b=conv2_b) layer2_r = Conv_with_input_para( rng, input=layer1.output_tensor_r, image_shape=(batch_size, 1, nkerns[0], ishape[1]), filter_shape=(nkerns[1], 1, filter_size_2[0], filter_size_2[1]), W=conv2_W, b=conv2_b) layer2_para = [conv2_W, conv2_b] layer3 = Average_Pooling_for_Top(rng, input_l=layer2_l.output, input_r=layer2_r.output, kern=nkerns[1], left_l=left_l, right_l=right_l, left_r=left_r, right_r=right_r, length_l=length_l + filter_size_2[1] - 1, length_r=length_r + filter_size_2[1] - 1, dim=maxSentLength + filter_size_2[1] - 1) #layer2=HiddenLayer(rng, input=layer1_out, n_in=nkerns[0]*2, n_out=hidden_size, activation=T.tanh) sum_uni_l = T.sum(layer0_l_input, axis=3).reshape((1, emb_size)) aver_uni_l = sum_uni_l / layer0_l_input.shape[3] norm_uni_l = sum_uni_l / T.sqrt((sum_uni_l**2).sum()) sum_uni_r = T.sum(layer0_r_input, axis=3).reshape((1, emb_size)) aver_uni_r = sum_uni_r / layer0_r_input.shape[3] norm_uni_r = sum_uni_r / T.sqrt((sum_uni_r**2).sum()) uni_cosine = cosine(sum_uni_l, sum_uni_r) aver_uni_cosine = cosine(aver_uni_l, aver_uni_r) uni_sigmoid_simi = debug_print( T.nnet.sigmoid(T.dot(norm_uni_l, norm_uni_r.T)).reshape((1, 1)), 'uni_sigmoid_simi') ''' linear=Linear(sum_uni_l, sum_uni_r) poly=Poly(sum_uni_l, sum_uni_r) sigmoid=Sigmoid(sum_uni_l, sum_uni_r) rbf=RBF(sum_uni_l, sum_uni_r) gesd=GESD(sum_uni_l, sum_uni_r) ''' eucli_1 = 1.0 / (1.0 + EUCLID(sum_uni_l, sum_uni_r)) #25.2% #eucli_1_exp=1.0/T.exp(EUCLID(sum_uni_l, sum_uni_r)) len_l = norm_length_l.reshape((1, 1)) len_r = norm_length_r.reshape((1, 1)) ''' len_l=length_l.reshape((1,1)) len_r=length_r.reshape((1,1)) ''' #length_gap=T.log(1+(T.sqrt((len_l-len_r)**2))).reshape((1,1)) #length_gap=T.sqrt((len_l-len_r)**2) #layer3_input=mts layer3_input = T.concatenate( [ #mts, uni_cosine, #eucli_1_exp,#uni_sigmoid_simi, #norm_uni_l-(norm_uni_l+norm_uni_r)/2,#uni_cosine, # layer1. output_cosine, #layer1.output_eucli_to_simi_exp,#layer1.output_sigmoid_simi,#layer1.output_vector_l-(layer1.output_vector_l+layer1.output_vector_r)/2,#layer1.output_cosine, # layer3.output_cosine, len_l, len_r, wmf ], axis=1) #, layer2.output, layer1.output_cosine], axis=1) #layer3_input=T.concatenate([mts,eucli, uni_cosine, len_l, len_r, norm_uni_l-(norm_uni_l+norm_uni_r)/2], axis=1) #layer3=LogisticRegression(rng, input=layer3_input, n_in=11, n_out=2) layer3 = LogisticRegression(rng, input=layer3_input, n_in=(1) + (1) + (1) + 2 + 2, n_out=2) #L2_reg =(layer3.W** 2).sum()+(layer2.W** 2).sum()+(layer1.W** 2).sum()+(conv_W** 2).sum() L2_reg = debug_print( (layer3.W**2).sum() + (conv2_W**2).sum(), 'L2_reg' ) #+(conv_W** 2).sum()+(layer1.W** 2).sum()++(embeddings**2).sum() cost_this = debug_print(layer3.negative_log_likelihood(y), 'cost_this') #+L2_weight*L2_reg cost = debug_print( (cost_this + cost_tmp) / update_freq + L2_weight * L2_reg, 'cost') #cost=debug_print((cost_this+cost_tmp)/update_freq, 'cost') test_model = theano.function( [index], [layer3.prop_for_posi, layer3_input, y], givens={ x_index_l: indices_test_l[index:index + batch_size], x_index_r: indices_test_r[index:index + batch_size], y: testY[index:index + batch_size], left_l: testLeftPad_l[index], right_l: testRightPad_l[index], left_r: testLeftPad_r[index], right_r: testRightPad_r[index], length_l: testLengths_l[index], length_r: testLengths_r[index], norm_length_l: normalized_test_length_l[index], norm_length_r: normalized_test_length_r[index], mts: mt_test[index:index + batch_size], wmf: wm_test[index:index + batch_size] }, on_unused_input='ignore') #params = layer3.params + layer2.params + layer1.params+ [conv_W, conv_b] params = layer3.params + layer2_para #+layer0_para accumulator = [] for para_i in params: eps_p = numpy.zeros_like(para_i.get_value(borrow=True), dtype=theano.config.floatX) accumulator.append(theano.shared(eps_p, borrow=True)) # create a list of gradients for all model parameters grads = T.grad(cost, params) updates = [] for param_i, grad_i, acc_i in zip(params, grads, accumulator): grad_i = debug_print(grad_i, 'grad_i') acc = acc_i + T.sqr(grad_i) updates.append( (param_i, param_i - learning_rate * grad_i / T.sqrt(acc))) #AdaGrad updates.append((acc_i, acc)) train_model = theano.function( [index, cost_tmp], cost, updates=updates, givens={ x_index_l: indices_train_l[index:index + batch_size], x_index_r: indices_train_r[index:index + batch_size], y: trainY[index:index + batch_size], left_l: trainLeftPad_l[index], right_l: trainRightPad_l[index], left_r: trainLeftPad_r[index], right_r: trainRightPad_r[index], length_l: trainLengths_l[index], length_r: trainLengths_r[index], norm_length_l: normalized_train_length_l[index], norm_length_r: normalized_train_length_r[index], mts: mt_train[index:index + batch_size], wmf: wm_train[index:index + batch_size] }, on_unused_input='ignore') train_model_predict = theano.function( [index], [cost_this, layer3.errors(y), layer3_input, y], givens={ x_index_l: indices_train_l[index:index + batch_size], x_index_r: indices_train_r[index:index + batch_size], y: trainY[index:index + batch_size], left_l: trainLeftPad_l[index], right_l: trainRightPad_l[index], left_r: trainLeftPad_r[index], right_r: trainRightPad_r[index], length_l: trainLengths_l[index], length_r: trainLengths_r[index], norm_length_l: normalized_train_length_l[index], norm_length_r: normalized_train_length_r[index], mts: mt_train[index:index + batch_size], wmf: wm_train[index:index + batch_size] }, on_unused_input='ignore') ############### # TRAIN MODEL # ############### print '... training' # early-stopping parameters patience = 500000000000000 # look as this many examples regardless patience_increase = 2 # wait this much longer when a new best is # found improvement_threshold = 0.995 # a relative improvement of this much is # considered significant validation_frequency = min(n_train_batches / 5, patience / 2) # go through this many # minibatche before checking the network # on the validation set; in this case we # check every epoch best_params = None best_validation_loss = numpy.inf best_iter = 0 test_score = 0. start_time = time.clock() epoch = 0 done_looping = False svm_max = 0.0 best_epoch = 0 while (epoch < n_epochs) and (not done_looping): epoch = epoch + 1 #for minibatch_index in xrange(n_train_batches): # each batch minibatch_index = 0 #shuffle(train_batch_start)#shuffle training data cost_tmp = 0.0 for batch_start in train_batch_start: # iter means how many batches have been runed, taking into loop iter = (epoch - 1) * n_train_batches + minibatch_index + 1 minibatch_index = minibatch_index + 1 #if epoch %2 ==0: # batch_start=batch_start+remain_train #time.sleep(0.5) #print batch_start if iter % update_freq != 0: cost_ij, error_ij, layer3_input, y = train_model_predict( batch_start) #print 'layer3_input', layer3_input cost_tmp += cost_ij error_sum += error_ij #print 'cost_acc ',cost_acc #print 'cost_ij ', cost_ij #print 'cost_tmp before update',cost_tmp else: cost_average = train_model(batch_start, cost_tmp) #print 'layer3_input', layer3_input error_sum = 0 cost_tmp = 0.0 #reset for the next batch #print 'cost_average ', cost_average #print 'cost_this ',cost_this #exit(0) #exit(0) if iter % n_train_batches == 0: print 'training @ iter = ' + str( iter) + ' average cost: ' + str( cost_average) + ' error: ' + str( error_sum) + '/' + str( update_freq) + ' error rate: ' + str( error_sum * 1.0 / update_freq) #if iter ==1: # exit(0) if iter % validation_frequency == 0: #write_file=open('log.txt', 'w') test_probs = [] test_y = [] test_features = [] for i in test_batch_start: prob_i, layer3_input, y = test_model(i) #test_losses = [test_model(i) for i in test_batch_start] test_probs.append(prob_i[0][0]) test_y.append(y[0]) test_features.append(layer3_input[0]) MAP, MRR = compute_map_mrr(rootPath + 'test_filtered.txt', test_probs) #now, check MAP and MRR print( ('\t\t\t\t\t\tepoch %i, minibatch %i/%i, test MAP of best ' 'model %f, MRR %f') % (epoch, minibatch_index, n_train_batches, MAP, MRR)) #now, see the results of LR #write_feature=open(rootPath+'feature_check.txt', 'w') train_y = [] train_features = [] count = 0 for batch_start in train_batch_start: cost_ij, error_ij, layer3_input, y = train_model_predict( batch_start) train_y.append(y[0]) train_features.append(layer3_input[0]) #write_feature.write(str(batch_start)+' '+' '.join(map(str,layer3_input[0]))+'\n') #count+=1 #write_feature.close() clf = svm.SVC(C=1.0, kernel='linear') clf.fit(train_features, train_y) results_svm = clf.decision_function(test_features) MAP_svm, MRR_svm = compute_map_mrr( rootPath + 'test_filtered.txt', results_svm) lr = LinearRegression().fit(train_features, train_y) results_lr = lr.predict(test_features) MAP_lr, MRR_lr = compute_map_mrr( rootPath + 'test_filtered.txt', results_lr) print '\t\t\t\t\t\t\tSVM, MAP: ', MAP_svm, ' MRR: ', MRR_svm, ' LR: ', MAP_lr, ' MRR: ', MRR_lr if patience <= iter: done_looping = True break #after each epoch, increase the batch_size if epoch % 2 == 1: update_freq = update_freq * 1 else: update_freq = update_freq / 1 #print 'Batch_size: ', update_freq end_time = time.clock() print('Optimization complete.') print('Best validation score of %f %% obtained at iteration %i,'\ 'with test performance %f %%' % (best_validation_loss * 100., best_iter + 1, test_score * 100.)) print >> sys.stderr, ('The code for file ' + os.path.split(__file__)[1] + ' ran for %.2fm' % ((end_time - start_time) / 60.))
def evaluate_lenet5(learning_rate=0.008, n_epochs=2000, nkerns=[400], batch_size=1, window_width=3, maxSentLength=30, emb_size=300, hidden_size=[300,10], margin=0.5, L2_weight=0.0001, Div_reg=0.0001, norm_threshold=5.0, use_svm=False): model_options = locals().copy() print "model options", model_options rootPath='/mounts/data/proj/wenpeng/Dataset/MicrosoftParaphrase/tokenized_msr/'; rng = numpy.random.RandomState(23455) datasets, word2id=load_msr_corpus_20161229(rootPath+'tokenized_train.txt', rootPath+'tokenized_test.txt', maxSentLength) vocab_size=len(word2id)+1 mtPath='/mounts/data/proj/wenpeng/Dataset/paraphraseMT/' mt_train, mt_test=load_mts(mtPath+'concate_15mt_train.txt', mtPath+'concate_15mt_test.txt') wm_train, wm_test=load_wmf_wikiQA(rootPath+'train_number_matching_scores.txt', rootPath+'test_number_matching_scores.txt') indices_train, trainY, trainLengths, normalized_train_length, trainLeftPad, trainRightPad= datasets[0] indices_train_l=indices_train[::2] indices_train_r=indices_train[1::2] trainLengths_l=trainLengths[::2] trainLengths_r=trainLengths[1::2] normalized_train_length_l=normalized_train_length[::2] normalized_train_length_r=normalized_train_length[1::2] trainLeftPad_l=trainLeftPad[::2] trainLeftPad_r=trainLeftPad[1::2] trainRightPad_l=trainRightPad[::2] trainRightPad_r=trainRightPad[1::2] indices_test, testY, testLengths,normalized_test_length, testLeftPad, testRightPad= datasets[1] indices_test_l=indices_test[::2] indices_test_r=indices_test[1::2] testLengths_l=testLengths[::2] testLengths_r=testLengths[1::2] normalized_test_length_l=normalized_test_length[::2] normalized_test_length_r=normalized_test_length[1::2] testLeftPad_l=testLeftPad[::2] testLeftPad_r=testLeftPad[1::2] testRightPad_l=testRightPad[::2] testRightPad_r=testRightPad[1::2] train_size = len(indices_train_l) test_size = len(indices_test_l) train_batch_start=range(train_size) test_batch_start=range(test_size) # indices_train_l=theano.shared(numpy.asarray(indices_train_l, dtype=theano.config.floatX), borrow=True) # indices_train_r=theano.shared(numpy.asarray(indices_train_r, dtype=theano.config.floatX), borrow=True) # indices_test_l=theano.shared(numpy.asarray(indices_test_l, dtype=theano.config.floatX), borrow=True) # indices_test_r=theano.shared(numpy.asarray(indices_test_r, dtype=theano.config.floatX), borrow=True) # indices_train_l=T.cast(indices_train_l, 'int32') # indices_train_r=T.cast(indices_train_r, 'int32') # indices_test_l=T.cast(indices_test_l, 'int32') # indices_test_r=T.cast(indices_test_r, 'int32') rand_values=random_value_normal((vocab_size, emb_size), theano.config.floatX, rng) # rand_values[0]=numpy.array(numpy.zeros(emb_size)) id2word = {y:x for x,y in word2id.iteritems()} word2vec=load_word2vec() rand_values=load_word2vec_to_init_new(rand_values, id2word, word2vec) embeddings=theano.shared(value=numpy.array(rand_values,dtype=theano.config.floatX), borrow=True)#theano.shared(value=rand_values, borrow=True) # allocate symbolic variables for the data # index = T.iscalar() x_index_l = T.imatrix() # now, x is the index matrix, must be integer x_index_r = T.imatrix() y = T.ivector() left_l=T.iscalar() right_l=T.iscalar() left_r=T.iscalar() right_r=T.iscalar() length_l=T.iscalar() length_r=T.iscalar() norm_length_l=T.fscalar() norm_length_r=T.fscalar() mts=T.fmatrix() wmf=T.fmatrix() # cost_tmp=T.fscalar() #x=embeddings[x_index.flatten()].reshape(((batch_size*4),maxSentLength, emb_size)).transpose(0, 2, 1).flatten() ishape = (emb_size, maxSentLength) # this is the size of MNIST images filter_size=(emb_size,window_width) #poolsize1=(1, ishape[1]-filter_size[1]+1) #????????????????????????????? length_after_wideConv=ishape[1]+filter_size[1]-1 ###################### # BUILD ACTUAL MODEL # ###################### print '... building the model' # Reshape matrix of rasterized images of shape (batch_size,28*28) # to a 4D tensor, compatible with our LeNetConvPoolLayer #layer0_input = x.reshape(((batch_size*4), 1, ishape[0], ishape[1])) layer0_l_input = embeddings[x_index_l.flatten()].reshape((batch_size,maxSentLength, emb_size)).dimshuffle(0, 'x', 2,1) layer0_r_input = embeddings[x_index_r.flatten()].reshape((batch_size,maxSentLength, emb_size)).dimshuffle(0, 'x', 2,1) conv_W, conv_b=create_conv_para(rng, filter_shape=(nkerns[0], 1, filter_size[0], filter_size[1])) conv_W_into_matrix=conv_W.reshape((conv_W.shape[0], conv_W.shape[2]*conv_W.shape[3])) #layer0_output = debug_print(layer0.output, 'layer0.output') layer0_l = Conv_with_input_para(rng, input=layer0_l_input, image_shape=(batch_size, 1, ishape[0], ishape[1]), filter_shape=(nkerns[0], 1, filter_size[0], filter_size[1]), W=conv_W, b=conv_b) layer0_r = Conv_with_input_para(rng, input=layer0_r_input, image_shape=(batch_size, 1, ishape[0], ishape[1]), filter_shape=(nkerns[0], 1, filter_size[0], filter_size[1]), W=conv_W, b=conv_b) layer0_l_output=debug_print(layer0_l.output, 'layer0_l.output') layer0_r_output=debug_print(layer0_r.output, 'layer0_r.output') layer0_l_output_maxpool = T.max(layer0_l.output_narrow_conv_out[:,:,:,left_l:], axis=3).reshape((1, nkerns[0])) layer0_r_output_maxpool = T.max(layer0_r.output_narrow_conv_out[:,:,:,left_r:], axis=3).reshape((1, nkerns[0])) layer1=Average_Pooling_for_Top(rng, input_l=layer0_l_output, input_r=layer0_r_output, kern=nkerns[0], left_l=left_l, right_l=right_l, left_r=left_r, right_r=right_r, length_l=length_l+filter_size[1]-1, length_r=length_r+filter_size[1]-1, dim=maxSentLength+filter_size[1]-1) sum_uni_l=T.sum(layer0_l_input[:,:,:,left_l:], axis=3).reshape((1, emb_size)) norm_uni_l=sum_uni_l/T.sqrt((sum_uni_l**2).sum()) sum_uni_r=T.sum(layer0_r_input[:,:,:,left_r:], axis=3).reshape((1, emb_size)) norm_uni_r=sum_uni_r/T.sqrt((sum_uni_r**2).sum()) uni_cosine=cosine(sum_uni_l, sum_uni_r) ''' linear=Linear(sum_uni_l, sum_uni_r) poly=Poly(sum_uni_l, sum_uni_r) sigmoid=Sigmoid(sum_uni_l, sum_uni_r) rbf=RBF(sum_uni_l, sum_uni_r) gesd=GESD(sum_uni_l, sum_uni_r) ''' eucli_1=1.0/(1.0+EUCLID(sum_uni_l, sum_uni_r))#25.2% #eucli_1=EUCLID(sum_uni_l, sum_uni_r) len_l=norm_length_l.reshape((1,1)) len_r=norm_length_r.reshape((1,1)) ''' len_l=length_l.reshape((1,1)) len_r=length_r.reshape((1,1)) ''' #length_gap=T.log(1+(T.sqrt((len_l-len_r)**2))).reshape((1,1)) #length_gap=T.sqrt((len_l-len_r)**2) #layer3_input=mts HL_layer_1_input=T.concatenate([ # mts, eucli_1, #uni_cosine,norm_uni_l-(norm_uni_l+norm_uni_r)/2,#uni_cosine, # uni_cosine, # sum_uni_l, # sum_uni_r, # sum_uni_l+sum_uni_r, 1.0/(1.0+EUCLID(layer0_l_output_maxpool, layer0_r_output_maxpool)), cosine(layer0_l_output_maxpool, layer0_r_output_maxpool), layer0_l_output_maxpool, layer0_r_output_maxpool, T.sqrt((layer0_l_output_maxpool-layer0_r_output_maxpool)**2+1e-10), layer1.output_eucli_to_simi, #layer1.output_cosine,layer1.output_vector_l-(layer1.output_vector_l+layer1.output_vector_r)/2,#layer1.output_cosine, # layer1.output_cosine, layer1.output_vector_l, layer1.output_vector_r, T.sqrt((layer1.output_vector_l-layer1.output_vector_r)**2+1e-10), # len_l, len_r layer1.output_attentions # wmf, ], axis=1)#, layer2.output, layer1.output_cosine], axis=1) HL_layer_1_input_with_extra=T.concatenate([#HL_layer_1_input, mts, len_l, len_r # wmf ], axis=1)#, layer2.output, layer1.output_cosine], axis=1) HL_layer_1_input_size=1+1+ 1+1+3* nkerns[0] +1+1+3*nkerns[0]+10*10 HL_layer_1_input_with_extra_size = HL_layer_1_input_size+15+2 HL_layer_1=HiddenLayer(rng, input=HL_layer_1_input, n_in=HL_layer_1_input_size, n_out=hidden_size[0], activation=T.tanh) HL_layer_2=HiddenLayer(rng, input=HL_layer_1.output, n_in=hidden_size[0], n_out=hidden_size[1], activation=T.tanh) LR_layer_input=T.concatenate([HL_layer_2.output, HL_layer_1.output, HL_layer_1_input],axis=1) LR_layer_input_with_extra=T.concatenate([HL_layer_2.output, HL_layer_1_input_with_extra],axis=1)#HL_layer_1.output, LR_layer=LogisticRegression(rng, input=LR_layer_input, n_in=HL_layer_1_input_size+hidden_size[0]+hidden_size[1], n_out=2) # LR_layer_input=HL_layer_2.output # LR_layer=LogisticRegression(rng, input=LR_layer_input, n_in=hidden_size, n_out=2) # layer3=LogisticRegression(rng, input=layer3_input, n_in=15+1+1+2+3, n_out=2) #L2_reg =(layer3.W** 2).sum()+(layer2.W** 2).sum()+(layer1.W** 2).sum()+(conv_W** 2).sum() L2_reg =debug_print((LR_layer.W** 2).sum()+(HL_layer_2.W** 2).sum()+(HL_layer_1.W** 2).sum()+(conv_W** 2).sum(), 'L2_reg')#+(layer1.W** 2).sum() # diversify_reg= Diversify_Reg(LR_layer.W.T)+Diversify_Reg(HL_layer_2.W.T)+Diversify_Reg(HL_layer_1.W.T)+Diversify_Reg(conv_W_into_matrix) cost_this =debug_print(LR_layer.negative_log_likelihood(y), 'cost_this')#+L2_weight*L2_reg cost=cost_this+L2_weight*L2_reg#+Div_reg*diversify_reg test_model = theano.function([x_index_l,x_index_r,y,left_l, right_l, left_r, right_r, length_l, length_r, norm_length_l, norm_length_r, mts,wmf], [LR_layer.errors(y), LR_layer.y_pred, LR_layer_input_with_extra, y], on_unused_input='ignore',allow_input_downcast=True) params = LR_layer.params+ HL_layer_2.params+HL_layer_1.params+[conv_W, conv_b]+[embeddings]#+[embeddings]# + layer1.params accumulator=[] for para_i in params: eps_p=numpy.zeros_like(para_i.get_value(borrow=True),dtype=theano.config.floatX) accumulator.append(theano.shared(eps_p, borrow=True)) # create a list of gradients for all model parameters grads = T.grad(cost, params) updates = [] for param_i, grad_i, acc_i in zip(params, grads, accumulator): clipped_grad = T.clip(grad_i, -0.5, 0.5) acc = acc_i + T.sqr(clipped_grad) updates.append((param_i, param_i - learning_rate * clipped_grad / T.sqrt(acc+1e-10))) #AdaGrad updates.append((acc_i, acc)) train_model = theano.function([x_index_l,x_index_r,y,left_l, right_l, left_r, right_r, length_l, length_r, norm_length_l, norm_length_r, mts,wmf], [cost,LR_layer.errors(y)], updates=updates, on_unused_input='ignore',allow_input_downcast=True) train_model_predict = theano.function([x_index_l,x_index_r,y,left_l, right_l, left_r, right_r, length_l, length_r, norm_length_l, norm_length_r, mts,wmf], [cost_this,LR_layer.errors(y), LR_layer_input_with_extra, y],on_unused_input='ignore',allow_input_downcast=True) ############### # TRAIN MODEL # ############### print '... training' # early-stopping parameters patience = 500000000000000 # look as this many examples regardless patience_increase = 2 # wait this much longer when a new best is # found improvement_threshold = 0.995 # a relative improvement of this much is best_params = None best_validation_loss = numpy.inf test_score = 0. start_time = time.clock() epoch = 0 done_looping = False max_acc=0.0 nn_max_acc=0.0 best_iter=0 cost_tmp=0.0 while (epoch < n_epochs) and (not done_looping): epoch = epoch + 1 #for minibatch_index in xrange(n_train_batches): # each batch minibatch_index=0 shuffle(train_batch_start)#shuffle training data for index in train_batch_start: # iter means how many batches have been runed, taking into loop iter = (epoch - 1) * train_size + minibatch_index +1 minibatch_index=minibatch_index+1 # if iter%update_freq != 0: # cost_ij, error_ij, layer3_input, y=train_model_predict(batch_start) # #print 'cost_ij: ', cost_ij # cost_tmp+=cost_ij # error_sum+=error_ij # else: cost_i, error_i= train_model(indices_train_l[index: index + batch_size], indices_train_r[index: index + batch_size], trainY[index: index + batch_size], trainLeftPad_l[index], trainRightPad_l[index], trainLeftPad_r[index], trainRightPad_r[index], trainLengths_l[index], trainLengths_r[index], normalized_train_length_l[index], normalized_train_length_r[index], mt_train[index: index + batch_size], wm_train[index: index + batch_size]) cost_tmp+=cost_i if iter < 6000 and iter %100 ==0: print 'training @ iter = '+str(iter)+' average cost: '+str(cost_tmp/iter) if iter >= 6000 and iter % 100 == 0: # if iter%100 ==0: print 'training @ iter = '+str(iter)+' average cost: '+str(cost_tmp/iter) test_losses=[] test_y=[] test_features=[] for index in test_batch_start: test_loss, pred_y, layer3_input, y=test_model(indices_test_l[index: index + batch_size], indices_test_r[index: index + batch_size], testY[index: index + batch_size], testLeftPad_l[index], testRightPad_l[index], testLeftPad_r[index], testRightPad_r[index], testLengths_l[index], testLengths_r[index], normalized_test_length_l[index], normalized_test_length_r[index], mt_test[index: index + batch_size], wm_test[index: index + batch_size]) #test_losses = [test_model(i) for i in test_batch_start] test_losses.append(test_loss) test_y.append(y[0]) test_features.append(layer3_input[0]) #write_file.write(str(pred_y[0])+'\n')#+'\t'+str(testY[i].eval())+ #write_file.close() test_score = numpy.mean(test_losses) test_acc = (1-test_score) * 100. if test_acc > nn_max_acc: nn_max_acc = test_acc print '\t\t\tepoch:', epoch, 'iter:', iter, 'current acc:', test_acc, 'nn_max_acc:', nn_max_acc #now, see the results of svm if use_svm: train_y=[] train_features=[] for index in train_batch_start: cost_ij, error_ij, layer3_input, y=train_model_predict(indices_train_l[index: index + batch_size], indices_train_r[index: index + batch_size], trainY[index: index + batch_size], trainLeftPad_l[index], trainRightPad_l[index], trainLeftPad_r[index], trainRightPad_r[index], trainLengths_l[index], trainLengths_r[index], normalized_train_length_l[index], normalized_train_length_r[index], mt_train[index: index + batch_size], wm_train[index: index + batch_size]) train_y.append(y[0]) train_features.append(layer3_input[0]) #write_feature.write(' '.join(map(str,layer3_input[0]))+'\n') #write_feature.close() clf = svm.SVC(kernel='linear')#OneVsRestClassifier(LinearSVC()) #linear 76.11%, poly 75.19, sigmoid 66.50, rbf 73.33 clf.fit(train_features, train_y) results=clf.predict(test_features) lr=LinearRegression().fit(train_features, train_y) results_lr=lr.predict(test_features) corr_count=0 corr_lr=0 test_size=len(test_y) for i in range(test_size): if results[i]==test_y[i]: corr_count+=1 if numpy.absolute(results_lr[i]-test_y[i])<0.5: corr_lr+=1 acc=corr_count*1.0/test_size acc_lr=corr_lr*1.0/test_size if acc > max_acc: max_acc=acc best_iter=iter if acc_lr> max_acc: max_acc=acc_lr best_iter=iter print '\t\t\t\tsvm acc: ', acc, 'LR acc: ', acc_lr, ' max acc: ', max_acc , ' at iter: ', best_iter if patience <= iter: done_looping = True break end_time = time.clock() print('Optimization complete.') print('Best validation score of %f %% obtained at iteration %i,'\ 'with test performance %f %%' % (best_validation_loss * 100., best_iter + 1, test_score * 100.)) print >> sys.stderr, ('The code for file ' + os.path.split(__file__)[1] + ' ran for %.2fm' % ((end_time - start_time) / 60.))